Mind the Gap: Measuring Generalization Performance Across Multiple Objectives

Feurer, Matthias; Eggensperger, Katharina; Bergman, Edward; Pfisterer, Florian; Bischl, Bernd und Hutter, Frank (2023): Mind the Gap: Measuring Generalization Performance Across Multiple Objectives. 21st International Symposium on Intelligent Data Analysis (IDA), Louvain la Neuve, Belgium, 12.-14.April 2023. Crémilleux, Bruno; Hess, Sibylle und Nijssen, Siegfried (Hrsg.): In: Advances in Intelligent Data Analysis XXI, Lecture Notes in Computer Science Bd. 13876 Cham: Springer. S. 130-142

Abstract

Modern machine learning models are often constructed taking into account multiple objectives, e.g., minimizing inference time while also maximizing accuracy. Multi-objective hyperparameter optimization (MHPO) algorithms return such candidate models, and the approximation of the Pareto front is used to assess their performance. In practice, we also want to measure generalization when moving from the validation to the test set. However, some of the models might no longer be Pareto-optimal which makes it unclear how to quantify the performance of the MHPO method when evaluated on the test set. To resolve this, we provide a novel evaluation protocol that allows measuring the generalization performance of MHPO methods and studying its capabilities for comparing two optimization experiments.

• Dokumententyp: Konferenzbeitrag (Paper)
• Fakultät: Mathematik, Informatik und Statistik > Statistik
• Themengebiete: 000 Informatik, Informationswissenschaft, allgemeine Werke > 004 Informatik ; 500 Naturwissenschaften und Mathematik > 510 Mathematik
• ISBN: 978-3-031-30046-2; 978-3-031-30047-9 ; 978-3-031-30048-6
• ISSN: 0302-9743
• Ort: Cham
• Sprache: Englisch